340.643.79
Bioinformatic Strategies for Microbiome Data
Location
Internet
Term
Summer Institute
Department
Epidemiology
Credit(s)
2
Academic Year
2023 - 2024
Instruction Method
Synchronous Online
Start Date
Monday, June 19, 2023
End Date
Friday, June 30, 2023
M, Tu, W, Th, F, 10:00am - 12:00pm
Auditors Allowed
No
Available to Undergraduate
No
Grading Restriction
Letter Grade or Pass/Fail
Course Instructor(s)
Contact Name
Frequency Schedule
One Year Only
Resources
Prerequisite
Students should have a working knowledge of statistics including familiarity with parametric and non-parametric testing and knowledge of how to read graphs displaying data distributions. An understanding of basic ecology including taxonomic levels is helpful.
Do you have microbiome sequencing data you need to analyze but don't know how to get started?
Are you interested in understanding why microbiome results never seem externally valid?
Introduces key steps for bioinformatic analysis of microbiome data from preparing the data for analysis to visualizing the results. Provides a foundation in ecological concepts including alpha and beta diversity. Explains different methods for finding microbes that differ between environments. Prepares students to plan their own analyses and interpret the results using lectures and hands-on data interpretation exercises.
Learning Objectives
Upon successfully completing this course, students will be able to:
- Justify the choice of a sequencing target to profile the human microbiome in different scenarios
- Explain strategies to maximize the external validity of results when constructing a feature table
- Compare and contrast the hypotheses tested by different metrics used to measure alpha and beta diversity
- Intrepret a PCoA plot and accompanying statistical tests
- Define compositional data in the context of the microbiome analysis and describe pros and cons of 3 ways to handle compositional data
- Identify and utilize tools and norms for reproducible analysis of microbiome data
Methods of Assessment
This course is evaluated as follows:
- 30% Homework
- 20% Lab Assignments
- 10% Interim Assessment
- 40% Final Project
Analysis will be taught through a series of hands on exercises on pre-computed data based on QIIME 2 and R. New analyses will be performed using a galaxy server for qiime2. Students will not be required to install additional software, nor are there limits on computing environments for this course.